As process windows shrink, semiconductor manufacturers may find it more and more difficult to maintain good yield and avoid excursions (deviations). The yield and device performance may also be more sensitive to process variations. For example, chamber matching based on wafer results and preventing chamber excursions are keys to managing tool variability and its effects on yield and device performance. Advanced statistical and machine learning methods combined with a deep knowledge of tool design can help build generalized models of variability in tools helping to match chambers and to predict excursions. These generalized prediction models can identify variables effecting yield (or causing defect) and can detect chamber excursions.
Typically, advanced statistical methods are used for building high fidelity (high quality) prediction models of performance indicators, for example, for virtual metrology (VM), yield, defects, and predictive maintenance (PdM). Developing high fidelity prediction models is challenging because of highly noisy and correlated input or process data, low signal to noise, and a high variability environment. The correlated data makes it difficult to accurately identify the cause of failure in data. Low signal to noise implies that information may be missing. A high variability process implies that prediction models are not generalized and quickly change.
Developing robust prediction models becomes more challenging due to the large number of input variables, high levels of multicollinearity, and noise. Reducing this input variable list to a smaller list which modeling techniques can handle is usually done using heuristics and past knowledge, which can be very time consuming and prone to errors and can result in less accurate predictive models.